手法を比較

	ベイズ遺伝子セット濃縮解析	マルチオミクス遺伝子セットエンリッチメント解析
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2004–2007	2005 (GSEA foundation); multi-omics extensions ~2013–2020
提唱者	Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework	Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s
種類	Probabilistic gene set enrichment method	Integrative enrichment analysis pipeline
原典	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
別名	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	multi-omics GSEA, integrated GSEA, cross-omics pathway enrichment, multi-layer GSEA
関連	6	6
概要	Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GSEA, the Bayesian approach models uncertainty in expression estimates explicitly, incorporates prior biological knowledge, and produces posterior probabilities of enrichment rather than raw p-values, enabling more principled inference especially in small-sample settings.	Multi-omics gene set enrichment analysis (multi-omics GSEA) is a computational pipeline that applies GSEA logic simultaneously across two or more molecular measurement layers — such as transcriptomics, proteomics, and metabolomics — to identify biological pathways or gene sets that are coordinately dysregulated across omics platforms. By integrating ranked molecular signatures from each layer, it reveals pathway-level convergence that no single omics platform could detect alone.